Level Analysis

Cycle 5 (6.17.22)

Guiding questions/tasks & format:
SCHOOL TYPE: How do tagging patterns differ among district, charter, independent, sws, virtual, microschool, homeschool, and hub schools?
* table of biggest differences between categories
* odds ratio
* visualization

Analysis Notes:

Independent cases * district = 78
* charter = 57
* independent = 20
* sws = 5
* virtual = 9
* microschool = 12
* homeschool = 5
* hub = 3

Multicoded cases * district AND charter = 4
* district AND sws = 5
* district AND virtual = 7
* district AND microschool = 1
* district AND homeschool = 1
* charter AND sws = 1
* charter AND virtual = 2
* charter AND hub = 1
* independent AND virtual = 1
* independent AND microschool = 7
* independent AND hub = 1
* sws AND virtual = 1
* sws AND microschool = 1
* virtual AND microschool = 1
* virutal AND homeschool = 1
* virtual AND hub = 1
* microschool AND homeschool = 1
* microschool AND hub = 1 * NONE = 5
* Boston Day And Evening Academy
* CodeRVA Regional High School
* The Metropolitan Regional Career And Technical Center
* Grant Beacon Middle School
* Utah Schools For The Deaf
* 0 NA values * total schools = 161 (5 NA manually recoded)

Focus on odds ratio plot for levels by Tuesday–other plots by this weekend

Further cleaning needed. Multi-coded and non-coded data:

Paring down to district, charter, and independent
* charter = 58
* district = 77
* independent/other = 26

Table of Differences

Largest Differences

District vs. Charter Barchart

Charter vs. Independent

Independent vs. District Barchart

Smallest Differences

Individual Type Analysis

Charter vs. non-Charter Bar Chart

District vs. non-District Barchart

Independent vs. non-Independent Barchart

Variation Across All Types

Variability Between All School Types

Save plots:

Models

Race and SES

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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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Controlling for Aggregate BIPOC Population

The second model uses the same demographic variables, but rather than drawing from black and hispanic percentages, it uses an overall BIPOC student percentage (as calculated by non-white percent). I recognize this may not be super useful for reporting, but I was interested to see if there were any significant differences between majority-majority and majority-minority schools when racial demographics are not disaggregated.

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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
## Chain 1: 
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
## Chain 2: 
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
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Controlling for Locale

## 
## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
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## SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
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Odds and Ends (7.6.22)

Outstanding questions to help build this out: How big are independent schools (enrollment) compared to charters & districts?
* would you rather have this reported as averages within each type or using bins?* What proportion of district schools are high schools? What proportion of charters are HS? What proportion of independent schools are HS? What proportion of charter schools are urban/suburban/rural, vs. district and independent schools?

What proportion of district/charter/independent schools are high schools?

What proportion of charter schools are urban/suburban/rural?

What proportion of district schools are urban/suburban/rural?

What proportion of independent schools are urban/suburban/rural?